Nonprofit developer of active implantable medical devices for underserved conditions
huMannity Medtec is a nonprofit medical device developer taking implantable technologies from concept through clinical-trial manufacturing. Its stack pairs embedded C++ on ARM Cortex-M with Altium Designer, LTspice and SolidWorks, while a Python testing toolchain (pytest, NumPy, SciPy) points to heavy investment in automated verification. Hiring is accelerating and engineering-led, concentrated at mid and senior levels in the United States.
huMannity Medtec, the Alfred E. Mann Foundation for Scientific Research, is a nonprofit organization founded in 1985 that develops medical technologies for conditions with limited treatment options and smaller patient populations. The organization handles the full development path in-house, from early ideas through prototyping, quality control and manufacturing of devices for clinical trials. It employs 51–200 people and is headquartered in Valencia, CA, with current hiring in the United States. Teams span engineering, manufacturing, quality and finance, supported by business systems such as Dynamics 365 and Power Platform.
huMannity Medtec uses C++ on ARM Cortex-M, Python with pytest, NumPy, SciPy and scikit-learn, MATLAB, SolidWorks, Altium Designer, LTspice, AutoCAD, Jira, Bitbucket and Dynamics 365.
Job posts point to active implantable medical device development, hardware-firmware-software integration, system safety and reliability testing, and Python-based automated test frameworks.
Yes. Hiring is accelerating and led by engineering, with additional roles in manufacturing, quality and finance, mostly mid and senior level in the United States.
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huMannity Medtec's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.